Add new SentenceTransformer model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

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+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:91044
+- loss:CosineSimilarityLoss
+base_model: hiiamsid/sentence_similarity_spanish_es
+widget:
+- source_sentence: ¿Cuánto debo pagar por la llave con código VA34P?
+  sentences:
+  - ¿La llave HY5P pertenece a qué marca?
+  - ¿Cuál es el valor actual de VA34P JMA?
+  - ¿Cuánto cuesta la llave ME4P?
+- source_sentence: ¿Me puedes decir cuánto vale TOYOTA (TOY43) 2 BOTONES HILUX TOYO04?
+  sentences:
+  - ¿Cuánto debo pagar por la llave con código CAB?
+  - ¿Qué llave tiene el código TOYO04?
+  - ¿Qué código tiene la LETRA D?
+- source_sentence: ¿Tienen disponible la NISSAN DER LOGO?
+  sentences:
+  - ¿Cuál es el valor actual de NISSAN DER LOGO?
+  - ¿La llave MZ13A pertenece a qué marca?
+  - ¿CARRIAGE TENSION SPRING 017-24 tiene un precio accesible?
+- source_sentence: ¿Qué código tiene la GLOBE DER PEQ MKS?
+  sentences:
+  - ¿Qué llave tiene el código YM021?
+  - ¿Qué llave tiene el código MER03?
+  - ¿Cuánto cuesta la llave VP095?
+- source_sentence: ¿Qué modelo corresponde al código YP107?
+  sentences:
+  - ¿La llave TE4 pertenece a qué marca?
+  - ¿Me puedes decir cuánto vale FANAL?
+  - ¿Cuánto cuesta la llave P123VE?
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on hiiamsid/sentence_similarity_spanish_es
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [hiiamsid/sentence_similarity_spanish_es](https://huggingface.co/hiiamsid/sentence_similarity_spanish_es). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [hiiamsid/sentence_similarity_spanish_es](https://huggingface.co/hiiamsid/sentence_similarity_spanish_es) <!-- at revision 66ab46adac3910bb6ea6085b962a25e49513b981 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("sd-dreambooth-library/mks-similarity")
+# Run inference
+sentences = [
+    '¿Qué modelo corresponde al código YP107?',
+    '¿Cuánto cuesta la llave P123VE?',
+    '¿La llave TE4 pertenece a qué marca?',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### Unnamed Dataset
+
+* Size: 91,044 training samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                          | sentence2                                                                         | label                                                          |
+  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                            | float                                                          |
+  | details | <ul><li>min: 10 tokens</li><li>mean: 17.16 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 17.26 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.51</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence1                                                          | sentence2                                                      | label            |
+  |:-------------------------------------------------------------------|:---------------------------------------------------------------|:-----------------|
+  | <code>¿CY1 HELLO KITTY CORAZONES tiene un precio accesible?</code> | <code>¿Cuánto cuesta la llave CY43?</code>                     | <code>0.0</code> |
+  | <code>¿Qué modelo corresponde al código OP12?</code>               | <code>¿Me puedes decir cuánto vale CHEVROLET GM29?</code>      | <code>0.0</code> |
+  | <code>¿YALE PERSONAJE HULK tiene un precio accesible?</code>       | <code>¿Cuánto debo pagar por la llave con código YP117?</code> | <code>1.0</code> |
+* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
+  ```json
+  {
+      "loss_fct": "torch.nn.modules.loss.MSELoss"
+  }
+  ```
+
+### Evaluation Dataset
+
+#### Unnamed Dataset
+
+* Size: 10,116 evaluation samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                          | sentence2                                                                          | label                                                         |
+  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:--------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                             | float                                                         |
+  | details | <ul><li>min: 10 tokens</li><li>mean: 17.08 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 17.33 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.5</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence1                                                     | sentence2                                                    | label            |
+  |:--------------------------------------------------------------|:-------------------------------------------------------------|:-----------------|
+  | <code>¿Cuál es el precio de la AM3 AMERICAN LOCK?</code>      | <code>¿Cuánto debo pagar por la llave con código AM3?</code> | <code>1.0</code> |
+  | <code>¿Cuánto debo pagar por la llave con código MAS9?</code> | <code>¿La llave MAS9 pertenece a qué marca?</code>           | <code>1.0</code> |
+  | <code>¿La llave YP113 pertenece a qué marca?</code>           | <code>¿Qué llave tiene el código E029?</code>                | <code>0.0</code> |
+* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
+  ```json
+  {
+      "loss_fct": "torch.nn.modules.loss.MSELoss"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 32
+- `num_train_epochs`: 2
+- `warmup_ratio`: 0.1
+- `fp16`: True
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 32
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 5e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 2
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: True
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `tp_size`: 0
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | Validation Loss |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0351 | 100  | 0.1663        | -               |
+| 0.0703 | 200  | 0.1023        | -               |
+| 0.1054 | 300  | 0.0807        | -               |
+| 0.1405 | 400  | 0.0723        | -               |
+| 0.1757 | 500  | 0.0614        | 0.0535          |
+| 0.2108 | 600  | 0.0569        | -               |
+| 0.2460 | 700  | 0.052         | -               |
+| 0.2811 | 800  | 0.0382        | -               |
+| 0.3162 | 900  | 0.0408        | -               |
+| 0.3514 | 1000 | 0.0358        | 0.0329          |
+| 0.3865 | 1100 | 0.0353        | -               |
+| 0.4216 | 1200 | 0.032         | -               |
+| 0.4568 | 1300 | 0.0303        | -               |
+| 0.4919 | 1400 | 0.0275        | -               |
+| 0.5271 | 1500 | 0.0263        | 0.0223          |
+| 0.5622 | 1600 | 0.0237        | -               |
+| 0.5973 | 1700 | 0.0215        | -               |
+| 0.6325 | 1800 | 0.0233        | -               |
+| 0.6676 | 1900 | 0.0198        | -               |
+| 0.7027 | 2000 | 0.022         | 0.0163          |
+| 0.7379 | 2100 | 0.0185        | -               |
+| 0.7730 | 2200 | 0.0178        | -               |
+| 0.8082 | 2300 | 0.0168        | -               |
+| 0.8433 | 2400 | 0.018         | -               |
+| 0.8784 | 2500 | 0.0158        | 0.0127          |
+| 0.9136 | 2600 | 0.0141        | -               |
+| 0.9487 | 2700 | 0.015         | -               |
+| 0.9838 | 2800 | 0.0131        | -               |
+| 1.0190 | 2900 | 0.0117        | -               |
+| 1.0541 | 3000 | 0.0106        | 0.0100          |
+| 1.0892 | 3100 | 0.0082        | -               |
+| 1.1244 | 3200 | 0.0088        | -               |
+| 1.1595 | 3300 | 0.0084        | -               |
+| 1.1947 | 3400 | 0.0087        | -               |
+| 1.2298 | 3500 | 0.0093        | 0.0079          |
+| 1.2649 | 3600 | 0.0106        | -               |
+| 1.3001 | 3700 | 0.0097        | -               |
+| 1.3352 | 3800 | 0.0074        | -               |
+| 1.3703 | 3900 | 0.0072        | -               |
+| 1.4055 | 4000 | 0.0094        | 0.0067          |
+| 1.4406 | 4100 | 0.0062        | -               |
+| 1.4758 | 4200 | 0.0072        | -               |
+| 1.5109 | 4300 | 0.0081        | -               |
+| 1.5460 | 4400 | 0.0075        | -               |
+| 1.5812 | 4500 | 0.0071        | 0.0059          |
+| 1.6163 | 4600 | 0.0049        | -               |
+| 1.6514 | 4700 | 0.0064        | -               |
+| 1.6866 | 4800 | 0.0072        | -               |
+| 1.7217 | 4900 | 0.0075        | -               |
+| 1.7569 | 5000 | 0.0062        | 0.0052          |
+| 1.7920 | 5100 | 0.0061        | -               |
+| 1.8271 | 5200 | 0.0059        | -               |
+| 1.8623 | 5300 | 0.0062        | -               |
+| 1.8974 | 5400 | 0.005         | -               |
+| 1.9325 | 5500 | 0.0068        | 0.0048          |
+| 1.9677 | 5600 | 0.0051        | -               |
+
+
+### Framework Versions
+- Python: 3.11.12
+- Sentence Transformers: 3.4.1
+- Transformers: 4.51.3
+- PyTorch: 2.6.0+cu124
+- Accelerate: 1.6.0
+- Datasets: 3.5.1
+- Tokenizers: 0.21.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,26 @@
+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.51.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 31002
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.4.1",
+    "transformers": "4.51.3",
+    "pytorch": "2.6.0+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5066705db61cf0c6636f994dc9a3f3acadd9093bb683232253bdb31d85b086ba
+size 439425888

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,65 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "4": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "5": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": false,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "max_length": 512,
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": false,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}